49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
"""GMLVQ example using all four dimensions of the Iris dataset."""
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import pytorch_lightning as pl
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import torch
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from prototorch.components import initializers as cinit
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from prototorch.datasets.abstract import NumpyDataset
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader
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from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
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from prototorch.models.glvq import GMLVQ
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if __name__ == "__main__":
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# Dataset
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x_train, y_train = load_iris(return_X_y=True)
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train_ds = NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
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# Hyperparameters
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hparams = dict(
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nclasses=3,
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prototypes_per_class=1,
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prototype_initializer=cinit.SMI(torch.Tensor(x_train),
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torch.Tensor(y_train)),
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input_dim=x_train.shape[1],
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latent_dim=2,
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lr=0.01,
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)
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# Initialize the model
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model = GMLVQ(hparams)
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# Model summary
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print(model)
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# Callbacks
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vis = VisSiameseGLVQ2D(x_train, y_train)
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# Namespace hook for the visualization to work
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model.backbone = model.omega_layer
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# Setup trainer
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trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
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# Training loop
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trainer.fit(model, train_loader)
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