66 lines
1.5 KiB
Python
66 lines
1.5 KiB
Python
"""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 torch.optim.lr_scheduler import ExponentialLR
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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.Iris(dims=[0, 2])
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
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# Hyperparameters
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hparams = dict(
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distribution={
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"num_classes": 3,
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"per_class": 4
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},
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lr=0.01,
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)
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# Initialize the model
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model = pt.models.GLVQ(
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hparams,
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optimizer=torch.optim.Adam,
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prototypes_initializer=pt.initializers.SMCI(train_ds),
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lr_scheduler=ExponentialLR,
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lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
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)
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# Compute intermediate input and output sizes
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model.example_input_array = torch.zeros(4, 2)
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# Callbacks
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vis = pt.models.VisGLVQ2D(data=train_ds)
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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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weights_summary="full",
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accelerator="ddp",
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)
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# Training loop
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trainer.fit(model, train_loader)
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# Manual save
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trainer.save_checkpoint("./glvq_iris.ckpt")
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# Load saved model
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new_model = pt.models.GLVQ.load_from_checkpoint(
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checkpoint_path="./glvq_iris.ckpt",
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strict=False,
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)
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print(new_model)
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