54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
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"""GLVQ example using the Iris 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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from sklearn.datasets import load_iris
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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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x_train, y_train = load_iris(return_X_y=True)
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x_train = x_train[:, [0, 2]]
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train_ds = pt.datasets.NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds,
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num_workers=0,
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batch_size=150)
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# Hyperparameters
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num_classes = 3
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prototypes_per_class = 2
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hparams = dict(
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distribution=(num_classes, prototypes_per_class),
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lr=0.05,
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variance=1,
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)
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# Initialize the model
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model = pt.models.probabilistic.RSLVQ(
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hparams,
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optimizer=torch.optim.Adam,
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prototype_initializer=pt.components.SSI(train_ds, noise=2),
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#prototype_initializer=pt.components.UniformInitializer(2),
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)
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# Callbacks
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vis = pt.models.VisGLVQ2D(data=(x_train, y_train), block=False)
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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],
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)
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# Training loop
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trainer.fit(model, train_loader)
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