53 lines
1.3 KiB
Python
53 lines
1.3 KiB
Python
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"""Median-LVQ 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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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(
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train_ds,
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batch_size=len(train_ds), # MedianLVQ cannot handle mini-batches
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)
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# Initialize the model
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model = pt.models.MedianLVQ(
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hparams=dict(distribution=(3, 2), lr=0.01),
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prototypes_initializer=pt.initializers.SSCI(train_ds),
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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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es = pl.callbacks.EarlyStopping(
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monitor="train_acc",
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min_delta=0.01,
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patience=5,
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mode="max",
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verbose=True,
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check_on_train_epoch_end=True,
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)
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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="full",
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)
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# Training loop
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trainer.fit(model, train_loader)
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